Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis
Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis
复制标题
通过车载监测表征热损失系数的灵敏度：案例研究分析
DOI：
10.3390/en12173322
复制
发表时间：
2019
期刊：
影响因子：
3.2
通讯作者：
D. Saelens
中科院分区：
文献类型：
--
作者：
Marieline Senave;S. Roels;S. Verbeke;Evi Lambie;D. Saelens
近年来，基于车载监测（OBM）和数据驱动建模相结合的方法来表征建筑物的竣工热损失系数（HLC）受到越来越多的关注。OBM在此定义为通过非侵入式传感器监测在用建筑物的能源消耗和内部气候。研究人员面临的主要挑战是确定所需的输入数据和适当的数据分析技术，以评估特定建筑类型的HLC，具有一定程度的准确性和/或在预算限制范围内。可以想象各种各样的表征技术，从应用于智能电表数据的简化稳态模型，到在完整的OBM数据集上识别的高级动态分析模型，这些数据集进一步丰富了几何信息，调查结果或现场检查。本文评估这些技术在不同的HLC估计结果的程度。为此，它进行了一个案例研究住宅的表征结果的敏感性分析。使用树结构定义了35个唯一的输入数据包。随后，四种不同的数据分析方法应用于这些集：稳态平均，线性回归和能量签名方法，和动态自回归与外源输入模型（ARX）。除了敏感性分析，本文比较了通过OBM表征确定的HLC值与理论计算值，并探讨了导致观察到的差异的因素。结果表明，高达26.9%的偏差可能会发生在所表征的建成HLC上，这取决于用于建立住宅内部温度的监测数据和先验信息的量。用于表示内部和太阳热增益的方法也被证明对HLC估计有显着影响。所选输入数据的影响高于所应用的数据分析方法。
Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.